An ART-like algorithm for constructing RBF neural networks

A novel algorithm, bases on the adaptive resonance theory (ART), is proposed to design the structure of radial basis function (RBF) neural networks in this paper. Based on the concept of “similarity”, this proposed ART-like algorithm can be utilized to construct the RBF neural network. The number of the hidden nodes is able to be adjusted in the learning process. Meanwhile, the activity of each hidden node can be owned through the initial width design to make the structure compact. Finally, three examples are employed to test the effectiveness of the proposed ART-like RBF (ART-RBF) neural network. The results indicate that this ART-RBF neural network has better comparable generalization performance with compact structure and fast training time.